186,840 research outputs found
Solving Large Extensive-Form Games with Strategy Constraints
Extensive-form games are a common model for multiagent interactions with
imperfect information. In two-player zero-sum games, the typical solution
concept is a Nash equilibrium over the unconstrained strategy set for each
player. In many situations, however, we would like to constrain the set of
possible strategies. For example, constraints are a natural way to model
limited resources, risk mitigation, safety, consistency with past observations
of behavior, or other secondary objectives for an agent. In small games,
optimal strategies under linear constraints can be found by solving a linear
program; however, state-of-the-art algorithms for solving large games cannot
handle general constraints. In this work we introduce a generalized form of
Counterfactual Regret Minimization that provably finds optimal strategies under
any feasible set of convex constraints. We demonstrate the effectiveness of our
algorithm for finding strategies that mitigate risk in security games, and for
opponent modeling in poker games when given only partial observations of
private information.Comment: Appeared in AAAI 201
Sparse Positional Strategies for Safety Games
We consider the problem of obtaining sparse positional strategies for safety
games. Such games are a commonly used model in many formal methods, as they
make the interaction of a system with its environment explicit. Often, a
winning strategy for one of the players is used as a certificate or as an
artefact for further processing in the application. Small such certificates,
i.e., strategies that can be written down very compactly, are typically
preferred. For safety games, we only need to consider positional strategies.
These map game positions of a player onto a move that is to be taken by the
player whenever the play enters that position. For representing positional
strategies compactly, a common goal is to minimize the number of positions for
which a winning player's move needs to be defined such that the game is still
won by the same player, without visiting a position with an undefined next
move. We call winning strategies in which the next move is defined for few of
the player's positions sparse.
Unfortunately, even roughly approximating the density of the sparsest
strategy for a safety game has been shown to be NP-hard. Thus, to obtain sparse
strategies in practice, one either has to apply some heuristics, or use some
exhaustive search technique, like ILP (integer linear programming) solving. In
this paper, we perform a comparative study of currently available methods to
obtain sparse winning strategies for the safety player in safety games. We
consider techniques from common knowledge, such as using ILP or SAT
(satisfiability) solving, and a novel technique based on iterative linear
programming. The results of this paper tell us if current techniques are
already scalable enough for practical use.Comment: In Proceedings SYNT 2012, arXiv:1207.055
Learning-Based Synthesis of Safety Controllers
We propose a machine learning framework to synthesize reactive controllers
for systems whose interactions with their adversarial environment are modeled
by infinite-duration, two-player games over (potentially) infinite graphs. Our
framework targets safety games with infinitely many vertices, but it is also
applicable to safety games over finite graphs whose size is too prohibitive for
conventional synthesis techniques. The learning takes place in a feedback loop
between a teacher component, which can reason symbolically about the safety
game, and a learning algorithm, which successively learns an overapproximation
of the winning region from various kinds of examples provided by the teacher.
We develop a novel decision tree learning algorithm for this setting and show
that our algorithm is guaranteed to converge to a reactive safety controller if
a suitable overapproximation of the winning region can be expressed as a
decision tree. Finally, we empirically compare the performance of a prototype
implementation to existing approaches, which are based on constraint solving
and automata learning, respectively
How to Handle Assumptions in Synthesis
The increased interest in reactive synthesis over the last decade has led to
many improved solutions but also to many new questions. In this paper, we
discuss the question of how to deal with assumptions on environment behavior.
We present four goals that we think should be met and review several different
possibilities that have been proposed. We argue that each of them falls short
in at least one aspect.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Safe Schedulability of Bounded-Rate Multi-Mode Systems
Bounded-rate multi-mode systems (BMMS) are hybrid systems that can switch
freely among a finite set of modes, and whose dynamics is specified by a finite
number of real-valued variables with mode-dependent rates that can vary within
given bounded sets. The schedulability problem for BMMS is defined as an
infinite-round game between two players---the scheduler and the
environment---where in each round the scheduler proposes a time and a mode
while the environment chooses an allowable rate for that mode, and the state of
the system changes linearly in the direction of the rate vector. The goal of
the scheduler is to keep the state of the system within a pre-specified safe
set using a non-Zeno schedule, while the goal of the environment is the
opposite. Green scheduling under uncertainty is a paradigmatic example of BMMS
where a winning strategy of the scheduler corresponds to a robust
energy-optimal policy. We present an algorithm to decide whether the scheduler
has a winning strategy from an arbitrary starting state, and give an algorithm
to compute such a winning strategy, if it exists. We show that the
schedulability problem for BMMS is co-NP complete in general, but for two
variables it is in PTIME. We also study the discrete schedulability problem
where the environment has only finitely many choices of rate vectors in each
mode and the scheduler can make decisions only at multiples of a given clock
period, and show it to be EXPTIME-complete.Comment: Technical report for a paper presented at HSCC 201
AbsSynthe: abstract synthesis from succinct safety specifications
In this paper, we describe a synthesis algorithm for safety specifications
described as circuits. Our algorithm is based on fixpoint computations,
abstraction and refinement, it uses binary decision diagrams as symbolic data
structure. We evaluate our tool on the benchmarks provided by the organizers of
the synthesis competition organized within the SYNT'14 workshop.Comment: In Proceedings SYNT 2014, arXiv:1407.493
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